By contrast, some work explores to improve the knowledge transferability without access to the target domain. For example, FT [1]} employs the affine transformation to simulate various feature distributions under different domains. Since this transformation is not directed to the target domain, it performs unstably on the different target domains. LRP [2]} applies the model-agnostic explanation-guided training strategy to improve the feature representations. ATA [3]} considers the task distribution space under the worst-case problem by adversarial task augmentation, which strengthens the robustness of the inductive bias. Different from them, we focus on how to employ limited labeled samples to construct metric spaces pointing to the target source. We propose a task-expansion-decomposition framework for CD-FSL, which obtains optimal parameters by target-oriented inner tasks.
